#!/usr/bin/env python3
import cv2
import rospy
from yolo_pkg.msg import Yolo
import time
import numpy as np
from queue import Queue
import qrcode
from drone_pkg.msg import QRCode
from pyzbar.pyzbar import decode
import time

#初始化节点
rospy.init_node("QR1", anonymous=True)
pub_yolo = rospy.Publisher("/QRCode_yolo", Yolo, queue_size=10)
rate = rospy.Rate(100)
pub = rospy.Publisher("/QRCode_detect", QRCode, queue_size=10)

from rknnlite.api import RKNNLite
from concurrent.futures import ThreadPoolExecutor, as_completed

data = -1


cap = cv2.VideoCapture('/dev/came1')
# cap = cv2.VideoCapture(0)
modelPath = r'/home/orangepi/catkin_ws/src/yolo_pkg/scripts/QRCode/QRCode.rknn'
 
QUANTIZE_ON = True
 
OBJ_THRESH, NMS_THRESH, IMG_SIZE = 0.75, 0.2, 640
CLASSES = ("QRCode",)


def Read_Decode_Pic(name, image, CamID):
    global data
    global pub

    # 标志变量，判断是否有二维码
    qr_found = False
    
    # 遍历二维码
    for code in decode(image):
        
        data = str(code.data)[2]
        print(data) 
        # pts_poly = np.array(code.polygon, np.int32)  
        # cv2.polylines(image, [pts_poly], True, (0, 255, 0), 5)  
        # pts_rect = code.rect
        # cv2.putText(image, data, (pts_rect[0], pts_rect[1]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        
        # # 发布消息
        # msg = QRCode()
        # msg.Data = str(data)
        # msg.CamID = CamID
        # msg.x = pts_rect[0]
        # msg.y = pts_rect[1]
        # msg.Width = pts_rect[2]
        # msg.Height = pts_rect[3]
        # pub.publish(msg)
        
        # # 设置标志，表示已找到二维码
        # qr_found = True

    # 如果找到二维码，返回 1；否则，返回 0
    if qr_found:
        return data
    else:
        return -1

       
#rknnpool
def initRKNN(rknnModel="./rknnModel/yolov5s.rknn", id=0):
    rknn_lite = RKNNLite()
    ret = rknn_lite.load_rknn(rknnModel)
    if ret != 0:
        print("Load RKNN rknnModel failed")
        exit(ret)
    if id == 0:
        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
    elif id == 1:
        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_1)
    elif id == 2:
        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_2)
    elif id == -1:
        ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0_1_2)
    else:
        ret = rknn_lite.init_runtime()
    if ret != 0:
        print("Init runtime environment failed")
        exit(ret)
    print(rknnModel, "\t\tdone")
    return rknn_lite
 
 
def initRKNNs(rknnModel="./rknnModel/yolov5s.rknn", TPEs=1):
    rknn_list = []
    for i in range(TPEs):
        rknn_list.append(initRKNN(rknnModel, i % 3))
    return rknn_list
 
 
class rknnPoolExecutor():
    def __init__(self, rknnModel, TPEs, func):
        self.TPEs = TPEs
        self.queue = Queue()
        self.rknnPool = initRKNNs(rknnModel, TPEs)
        self.pool = ThreadPoolExecutor(max_workers=TPEs)
        self.func = func
        self.num = 0
 
    def put(self, frame):
        self.queue.put(self.pool.submit(
            self.func, self.rknnPool[self.num % self.TPEs], frame))
        self.num += 1
 
    def get(self):
        if self.queue.empty():
            return None, False
        temp = []
        temp.append(self.queue.get())
        for frame in as_completed(temp):
            return frame.result(), True
 
    def release(self):
        self.pool.shutdown()
        for rknn_lite in self.rknnPool:
            rknn_lite.release()

 
def sigmoid(x):
    return 1 / (1 + np.exp(-x))
 
 
def xywh2xyxy(x):
    # Convert [x, y, w, h] to [x1, y1, x2, y2]
    y = np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y
 
 
def process(input, mask, anchors):
 
    anchors = [anchors[i] for i in mask]
    grid_h, grid_w = map(int, input.shape[0:2])
 
    box_confidence = sigmoid(input[..., 4])
    box_confidence = np.expand_dims(box_confidence, axis=-1)
 
    box_class_probs = sigmoid(input[..., 5:])
 
    box_xy = sigmoid(input[..., :2])*2 - 0.5
 
    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
    grid = np.concatenate((col, row), axis=-1)
    box_xy += grid
    box_xy *= int(IMG_SIZE/grid_h)
 
    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
    box_wh = box_wh * anchors
 
    box = np.concatenate((box_xy, box_wh), axis=-1)
 
    return box, box_confidence, box_class_probs
 
 
def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.
    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """
    boxes = boxes.reshape(-1, 4)
    box_confidences = box_confidences.reshape(-1)
    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
 
    _box_pos = np.where(box_confidences >= OBJ_THRESH)
    boxes = boxes[_box_pos]
    box_confidences = box_confidences[_box_pos]
    box_class_probs = box_class_probs[_box_pos]
 
    class_max_score = np.max(box_class_probs, axis=-1)
    classes = np.argmax(box_class_probs, axis=-1)
    _class_pos = np.where(class_max_score >= OBJ_THRESH)
 
    boxes = boxes[_class_pos]
    classes = classes[_class_pos]
    scores = (class_max_score * box_confidences)[_class_pos]
 
    return boxes, classes, scores
 
 
def nms_boxes(boxes, scores):
    """Suppress non-maximal boxes.
    # Arguments
        boxes: ndarray, boxes of objects.
        scores: ndarray, scores of objects.
    # Returns
        keep: ndarray, index of effective boxes.
    """
    x = boxes[:, 0]
    y = boxes[:, 1]
    w = boxes[:, 2] - boxes[:, 0]
    h = boxes[:, 3] - boxes[:, 1]
 
    areas = w * h
    order = scores.argsort()[::-1]
 
    keep = []
    while order.size > 0:
        i = order[0]
        keep.append(i)
 
        xx1 = np.maximum(x[i], x[order[1:]])
        yy1 = np.maximum(y[i], y[order[1:]])
        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
 
        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
        inter = w1 * h1
 
        ovr = inter / (areas[i] + areas[order[1:]] - inter)
        inds = np.where(ovr <= NMS_THRESH)[0]
        order = order[inds + 1]
    keep = np.array(keep)
    return keep
 
 
def yolov5_post_process(input_data):
    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
               [59, 119], [116, 90], [156, 198], [373, 326]]
 
    boxes, classes, scores = [], [], []
    for input, mask in zip(input_data, masks):
        b, c, s = process(input, mask, anchors)
        b, c, s = filter_boxes(b, c, s)
        boxes.append(b)
        classes.append(c)
        scores.append(s)
 
    boxes = np.concatenate(boxes)
    boxes = xywh2xyxy(boxes)
    classes = np.concatenate(classes)
    scores = np.concatenate(scores)
 
    nboxes, nclasses, nscores = [], [], []
    for c in set(classes):
        inds = np.where(classes == c)
        b = boxes[inds]
        c = classes[inds]
        s = scores[inds]
 
        keep = nms_boxes(b, s)
 
        nboxes.append(b[keep])
        nclasses.append(c[keep])
        nscores.append(s[keep])
 
    if not nclasses and not nscores:
        return None, None, None
 
    boxes = np.concatenate(nboxes)
    classes = np.concatenate(nclasses)
    scores = np.concatenate(nscores)
 
    return boxes, classes, scores
 
 
def draw(image, boxes, scores, classes, data):
    for box, score, cl in zip(boxes, scores, classes):
        top, left, right, bottom = box
        top = int(top)
        left = int(left)
        right = int(right)
        bottom = int(bottom)

        # 绘制边界框
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
        # 显示类别和分数
        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
                    (top, left - 6),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    0.6, (0, 0, 255), 2)

        # 计算中心点坐标
        center_x = (top + right) // 2
        center_y = (left + bottom) // 2

        # 在中心绘制红点
        cv2.circle(image, (center_x, center_y), 5, (0, 0, 255), -1)  # 红色圆点

        # 在红点旁显示坐标和data值
        cv2.putText(image, f'Center: ({center_x}, {center_y}) Data: {data}', 
                    (center_x + 10, center_y - 10), 
                    cv2.FONT_HERSHEY_SIMPLEX, 
                    0.6, (0, 0, 255), 2)

 
 
def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
    shape = im.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)
 
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
 
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - \
        new_unpad[1]  # wh padding
 
    dw /= 2  # divide padding into 2 sides
    dh /= 2
 
    if shape[::-1] != new_unpad:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    im = cv2.copyMakeBorder(im, top, bottom, left, right,
                            cv2.BORDER_CONSTANT, value=color)  # add border
    return im, ratio, (dw, dh)
 
def myFunc(rknn_lite, IMG):
    img = cv2.cvtColor(IMG, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
    outputs = rknn_lite.inference(inputs=[img])
 
    input0_data = outputs[0]
    input1_data = outputs[1]
    input2_data = outputs[2]
 
    input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
    input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
    input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
 
    input_data = list()
    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
 
    #识别在这里
    boxes, classes, scores = yolov5_post_process(input_data)
 
    img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    if boxes is not None:
        data =  Read_Decode_Pic('1', img, 1) 
        draw(img_1, boxes, scores, classes, data)
        for i, (cls, score) in enumerate(zip(classes, scores)):
            if (data != -1):
                box = boxes[i]
                x_min, y_min, x_max, y_max = box

                # 计算中心坐标
                center_x = (x_min + x_max) / 2
                center_y = (y_min + y_max) / 2

                msg = Yolo()
                msg.Class = str(cls)
                msg.x = int(center_x)
                msg.y = int(center_y)
                msg.Confidence = score
                msg.cam_id = 1
                pub_yolo.publish(msg)

    return img_1


if __name__ == "__main__":
    # 线程数
    TPEs = 3
    # 初始化rknn池
    pool = rknnPoolExecutor(
        rknnModel=modelPath,
        TPEs=TPEs,
        func=myFunc)
    
    # 初始化异步所需要的帧
    if (cap.isOpened()):
        for i in range(TPEs + 1):
            ret, frame = cap.read()
            if not ret:
                cap.release()
                del pool
                exit(-1)
            pool.put(frame)

    while (not rospy.is_shutdown()):
        ret, frame = cap.read()
        # try:
        #     Read_Decode_Pic('1', frame, 1)     
        # except Exception as e:
        #     # print(f"Error: {e}")
        #     pass
        t1 = time.time()
        pool.put(frame)
        frame, flag = pool.get()
        cv2.imshow('Yolo_1', frame)

        cv2.waitKey(1)

    cap.release()
    cv2.destroyAllWindows()
    pool.release()